arXiv subjects

Ishaan R Kale

Publications and source records attributed to Ishaan R Kale.

5 recordsLinked to original sources

arXiv2025

Snail Homing and Mating Search Algorithm for Weight Optimization of Stepped-Transmission Shaft

In this paper, the steeped-transmission shaft design problem is proposed for weight optimization. The bio-inspired search-based Snail Homing and Mating Search (SHMS) algorithm is utilized to solve the problem. It is inspired by the social behaviour of snails and their inherent nature of finding better homes, and mate. The proposed steeped-transmission shaft design problem is modelled considering the fatigue loading, combined bending, torsion loads, and the principle of Modified Goodman criteria. The forces diagram and the bending moment diagrams are obtained using the MDSOLIDS software. The forces and bending moment are then used to mathematical model the objective function and constraints. The SHMS algorithm has yielded the desired solution with reasonable computational cost. The constraints are handled using a static penalty function approach. The statistical results obtained using SHMS algorithm are further used for generating CAD model. The analysis is carried out in ANSYS Workbench. Further, the deflection obtained from SHMS algorithm and ANSYS Workbench are compared and results are discussed in details.

cs.NE↗

arXiv2024

Hybrid Firefly-Genetic Algorithm for Single and Multi-dimensional 0-1 Knapsack Problems

This paper addresses the challenges faced by algorithms, such as the Firefly Algorithm (FA) and the Genetic Algorithm (GA), in constrained optimization problems. While both algorithms perform well for unconstrained problems, their effectiveness diminishes when constraints are introduced due to limitations in exploration, exploitation, and constraint handling. To overcome these challenges, a hybrid FAGA algorithm is proposed, combining the strengths of both algorithms. The hybrid algorithm is validated by solving unconstrained benchmark functions and constrained optimization problems, including design engineering problems and combinatorial problems such as the 0-1 Knapsack Problem. The proposed algorithm delivers improved solution accuracy and computational efficiency compared to conventional optimization algorithm. This paper outlines the development and structure of the hybrid algorithm and demonstrates its effectiveness in handling complex optimization problems.

cs.NE↗

arXiv2023

Snail Homing and Mating Search Algorithm: A Novel Bio-Inspired Metaheuristic Algorithm

In this paper, a novel Snail Homing and Mating Search (SHMS) algorithm is proposed. It is inspired from the biological behaviour of the snails. Snails continuously travels to find food and a mate, leaving behind a trail of mucus that serves as a guide for their return. Snails tend to navigate by following the available trails on the ground and responding to cues from nearby shelter homes. The proposed SHMS algorithm is investigated by solving several unimodal and multimodal functions. The solutions are validated using standard statistical tests such as two-sided and pairwise signed rank Wilcoxon test and Friedman rank test. The solution obtained from the SHMS algorithm exhibited superior robustness as well as search space exploration capabilities within the less computational cost. The real-world application of SHMS algorithm is successfully demonstrated in the engineering design domain by solving three cases of design and economic optimization shell and tube heat exchanger problem. The objective function value and other statistical results obtained using SHMS algorithm are compared with other well-known metaheuristic algorithms.

cs.NE↗

arXiv2023

Hybrid ACO-CI Algorithm for Beam Design problems

A range of complicated real-world problems have inspired the development of several optimization methods. Here, a novel hybrid version of the Ant colony optimization (ACO) method is developed using the sample space reduction technique of the Cohort Intelligence (CI) Algorithm. The algorithm is developed, and accuracy is tested by solving 35 standard benchmark test functions. Furthermore, the constrained version of the algorithm is used to solve two mechanical design problems involving stepped cantilever beams and I-section beams. The effectiveness of the proposed technique of solution is evaluated relative to contemporary algorithmic approaches that are already in use. The results show that our proposed hybrid ACO-CI algorithm will take lesser number of iterations to produce the desired output which means lesser computational time. For the minimization of weight of stepped cantilever beam and deflection in I-section beam a proposed hybrid ACO-CI algorithm yielded best results when compared to other existing algorithms. The proposed work could be investigate for variegated real world applications encompassing domains of engineering, combinatorial and health care problems.

cs.NE↗

arXiv2022

A socio-physics based hybrid metaheuristic for solving complex non-convex constrained optimization problems

Several Artificial Intelligence based heuristic and metaheuristic algorithms have been developed so far. These algorithms have shown their superiority towards solving complex problems from different domains. However, it is necessary to critically validate these algorithms for solving real-world constrained optimization problems. The search behavior in those problems is different as it involves large number of linear, nonlinear and non-convex type equality and inequality constraints. In this work a 57 real-world constrained optimization problems test suite is solved using two constrained metaheuristic algorithms originated from a socio-based Cohort Intelligence (CI) algorithm. The first CI-based algorithm incorporates a self-adaptive penalty function approach i.e., CI-SAPF. The second algorithm combines CI-SAPF with the intrinsic properties of the physics-based Colliding Bodies Optimization (CBO) referred to CI-SAPF-CBO. The results obtained from CI-SAPF and CI-SAPF-CBO are compared with other constrained optimization algorithms. The superiority of the proposed algorithms is discussed in details followed by future directions to evolve the constrained handling techniques.

math.OC↗